Binary tensor-states in AI computation graphs:
While one computation unit in an AI computation graph produces real-value numbers, they can be binarized by thresholding at zero during measurement extraction. The thresholding operation reduces the real-value variability to \(\lbrace 0, 1\rbrace \)  by discretizing the output real values.
One can model the discretization step according to Hopfield Neural Networks (HNN) [1]} where a computation unit is viewed as an associative content-addressable memory for storing information bits about training data points [2]}. The HNN model was motivated by collective properties of neurons in neurobiology adapted to integrated circuits [1]}, and the more recent work on dense associative memory [4]} and Hopfield layers [5]}. The thresholding value at zero is motivated by zero-centered normalizations of input data and the majority of nonlinear activation functions being centered around zero (i.e., tanh, sigmoid, rectified linear unit (ReLU), or leaky ReLU).
